dataset-datasheet
Document a dataset so others know what it is, how it was made, and when not to use it. Use when asked to write a datasheet for a dataset, document training/eval data, or assess whether a dataset is fit for a use. Produces a datasheet — motivation, composition, collection process, preprocessing, recommended uses & limits, distribution, and maintenance.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/dataset-datasheet && cp -r /tmp/dataset-datasheet/plugins/pm-ai/skills/dataset-datasheet ~/.claude/skills/dataset-datasheetSKILL.md
# Dataset Datasheet Skill Models inherit the flaws of their data, and most data debt is invisible because nobody wrote down where the data came from. A datasheet is that record: how the dataset was collected, what's in it, what's missing, and what it should *not* be used for. It's the difference between a reusable asset and a liability. ## Required Inputs Ask for these only if they aren't already provided: - **Dataset name, version, owner** and what it's used for today. - **Motivation** — why it was created and for what task. - **Composition** — what an instance is, how many, fields/labels, and time range. - **Collection** — sources, method (scraped, logged, purchased, annotated), and consent/licensing basis. - **Known issues** — gaps, imbalances, label noise, sensitive attributes, duplicates. ## Output Format ### Datasheet: [dataset] v[version] **Owner:** [team] · **Created:** [date] · **License:** [license] **1. Motivation** — why this dataset exists, the task it serves, and who funded/created it. **2. Composition** - What a single instance represents; total count; the schema (fields, label definitions). - Class/label balance and key distributions (and notable skews). - **Sensitive attributes** present (directly or by proxy), and whether individuals are identifiable. - Known missing data, duplicates, or noise. **3. Collection process** — sources, mechanism (scrape/log/survey/annotation), time window, sampling strategy, and the **legal/consent basis** (license, ToS, opt-in). **4. Preprocessing / labelling** — cleaning, dedup, filtering, and how labels were produced (who annotated, guidelines, inter-annotator agreement). **5. Recommended uses & limits** - **Appropriate uses:** tasks this data supports well. - **Do not use for:** tasks where its biases/gaps would cause harm or invalid results. **6. Distribution & access** — who can use it, how it's shared, and tenancy/PII handling. **7. Maintenance** — owner, update cadence, versioning, and how errors get reported and fixed. ## Quality Checks - [ ] The collection method and **legal/consent basis** are stated — not assumed - [ ] Class balance and key distribution skews are quantified, not hand-waved - [ ] Sensitive attributes (and proxies for them) are identified explicitly - [ ] "Do not use for" lists concrete tasks where the data would mislead - [ ] Label provenance is documented (who labelled, with what guidelines, and agreement level) - [ ] An owner and update/error-reporting process are named ## Anti-Patterns - [ ] Do not describe only the happy-path contents — the gaps, skews, and noise are what cause model failures - [ ] Do not omit the consent/licensing basis — "we scraped it" is a legal and ethical liability if undocumented - [ ] Do not ignore proxy variables — removing race/gender columns doesn't remove the bias if zip code or name encodes it - [ ] Do not present label quality as perfect — state who labelled it and the agreement rate, or note it's unmeasured - [ ] Do not leave the dataset ownerless — an unmaintained dataset silently rots as the world changes ## Based On Datasheets for Datasets (Gebru et al., 2018) and data-documentation practice in responsible-AI reviews.
Design a 360-degree feedback survey or write a structured 360 feedback report. Use when asked to build a 360 feedback process, write 360 feedback for a colleague, design a feedback survey, or produce a feedback report. Produces either a complete survey instrument with rating scales and open-ended questions, or a structured narrative feedback report with themes, strengths, and development areas.
Decode a 401k or workplace retirement plan — the real cost of its funds, the match's fine print, vesting math, and the plan features worth using or avoiding. Use when someone asks 'is my 401k any good', 'decode my 401k plan', 'which funds should I look at', or 'what fees am I paying'. Produces a fee decode in dollars-over-time, match and vesting math, a fund-lineup triage by cost, and the questions for HR or the plan administrator.
Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.
Analyse a finished A/B test and write the readout — the result, whether it's statistically and practically significant, what it means, and the ship/no-ship call. Use when asked to analyse experiment results, write an A/B test readout, interpret test data, or decide whether to ship a variant. Produces a clear verdict with the lift and confidence, segment cuts, the risks (peeking, novelty, sample), and a recommendation. Distinct from planning a test — this reads results.
Generate a WCAG 2.2 accessibility audit checklist and remediation suggestions for any UI or design. Use when asked to audit for accessibility, check WCAG compliance, review a design for a11y issues, or create an accessibility remediation plan. Produces a prioritised checklist with pass/fail assessments and specific fixes.
Plan a trip that actually works with a disability or access need — confirm real accessibility (not just 'accessible' labels), book the assistance in advance, plan for equipment and medication, and build in the contingencies for when access breaks down. Use when someone says 'plan an accessible trip', 'travelling with a wheelchair/disability', 'book assistance for my flight', or 'will this hotel actually work for me'. Produces an access-verified itinerary, an assistance-booking checklist, an equipment/medication plan, and contingency scripts. Verify specifics with providers.
Request a reasonable accommodation at work or in education — frame it around the barrier and the adjustment (not your diagnosis), cite the right process, and navigate the back-and-forth constructively. Use when someone says 'I need a workplace accommodation', 'request reasonable adjustments', 'ADA/Equality Act accommodation', or 'how do I ask for accommodations for my disability/condition'. Produces the request letter, a barriers-and-adjustments map, disclosure guidance, and a plan for the interactive process. Not legal advice — routes to the formal process and to advocacy where needed.
Build a structured account plan for any key customer or target account. Use when asked to create an account plan, key account strategy, strategic account review, or territory plan. Produces a complete account plan with relationship map, growth opportunities, risks, and 90-day action plan.